--- title: OCO2 - Reproduce F. Chevallier's figures keywords: fastai sidebar: home_sidebar ---
{% raw %}
{% endraw %}

Open In Colab

{% raw %} 
!apt-get install libgeos-3.5.0
!apt-get install libgeos-dev
!pip install https://github.com/matplotlib/basemap/archive/master.zip
{% endraw %}

Introduction

Using Data from OCO-2 Satellite, issued by the NASA.

We here try to reproduce the results from this paper by F. Chevallier, trying to "[observe] carbon dioxide emissions over China's cities with the Orbiting Carbon Observatory-2".

//TODO: Explanation

{% raw %} 
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from mpl_toolkits.basemap import Basemap  #Imported directly from the github repository

/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
  import pandas.util.testing as tm
{% endraw %}

Show Data on the map

draw_map: Function to draw the map and the observations (relief style). The column names can be specified in the arguments.

Parameters:

  • (DataFrame) data: the dataset to map.
  • (string) x : the name of the longitude column. default: 'longitide'
  • (string) y: the name of the latitude column. default: 'latitude'
  • (string) c: the name of the XCO2 column (or other measure wanted to be plotted). default: 'xco2'
  • (int) lon_min : the minimum longitude. default: -180
  • (int) lon_max: the maximum longitude. default: 180
  • (int) lat_min: the minimum latitude. default: -90
  • (int) lat_max: the maximum latitude. default: 90
  • (int) size_point: size of the point to plot (useful if we zoom in). default: 1
  • (Bool) frontier: whether or not to draw the countries borders. default: False
{% raw %} 
def draw_map(data, x="longitude", y="latitude", c="xco2", lon_min=-180, lon_max=180, lat_min=-90, lat_max=90, size_point=1, frontier=False):

    plt.figure(figsize=(15, 10), edgecolor='w')
    m = Basemap(llcrnrlat=lat_min, urcrnrlat=lat_max, llcrnrlon=lon_min, urcrnrlon=lon_max)
    
    m.shadedrelief()
    
    parallels = np.arange(-80.,81,10.)
    m.drawparallels(parallels,labels=[False,True,True,False])

    meridians = np.arange(10.,351.,20.)
    m.drawmeridians(meridians,labels=[True,False,False,True])

    normal = matplotlib.colors.LogNorm(vmin=data[c].min(), vmax=data[c].max())

    m.scatter(data[x], data[y], c=data[c], cmap=plt.cm.jet, s=size_point, norm=normal)

    if (frontier):
      m.drawcountries(linewidth=0.5)
      m.drawcoastlines(linewidth=0.7)

    plt.show()
{% endraw %}
haversine: Function to calculate the great circle distance between two points on the earth given longitude and latitude.

Parameters:

  • (int) lon1 : the longitude of the first point.
  • (int) lon2: the latitude of the first point.
  • (int) lat1: the longitude of the second point.
  • (int) lat2: the latitude of the second point.

Return:

  • (int) the distance (in kilometers)
{% raw %} 
from math import radians, cos, sin, asin, sqrt

def haversine_formula(lon1, lat1, lon2, lat2):
    # convert decimal degrees to radians 
    lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])

    # haversine formula 
    dlon = lon2 - lon1 
    dlat = lat2 - lat1 
    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
    c = 2 * asin(sqrt(a)) 
    r = 6371 # Radius of earth in kilometers. Use 3956 for miles
    return c * r

def haversine(row, lat, lon):
    return haversine_formula(lon, lat, row['longitude'], row['latitude'])
{% endraw %}

Retieve Data

Sample data can be accessed freely on the NASA Database, among other open data from several NASA sattelites.

We will be using CSV aggregated by Benoit Courty here.

{% raw %} 
data_1610 = pd.read_csv("http://courty.fr/OCO2/oco2_1610.csv", sep=";")
data_1705 = pd.read_csv("http://courty.fr/OCO2/oco2_1705.csv", sep=";")
data_1803 = pd.read_csv("http://courty.fr/OCO2/oco2_1803.csv", sep=";")
data_1805 = pd.read_csv("http://courty.fr/OCO2/oco2_1805.csv", sep=";")
data_1808 = pd.read_csv("http://courty.fr/OCO2/oco2_1808.csv", sep=";")
data_1809 = pd.read_csv("http://courty.fr/OCO2/oco2_1809.csv", sep=";")

data_1610.head()
sounding_id latitude longitude xco2 xco2_uncert orbit windspeed_u windspeed_v
0 2016100101110037 -51.334530 -168.044601 401.371155 0.705995 11967 10.771300 5.545009
1 2016100101110038 -51.333263 -168.072342 401.515686 0.710246 11967 10.760405 5.543176
2 2016100101110073 -51.320339 -167.939957 401.222321 0.683168 11967 10.800521 5.544136
3 2016100101110074 -51.319645 -167.967422 401.089478 0.656449 11967 10.790007 5.542420
4 2016100101110078 -51.315422 -168.077682 401.365204 0.592020 11967 10.746749 5.534678
{% endraw %} {% raw %} 
data_1705.describe()
sounding_id latitude longitude xco2 xco2_uncert orbit windspeed_u windspeed_v
count 2.593966e+06 2.593966e+06 2.593966e+06 2.593966e+06 2.593966e+06 2.593966e+06 2.593966e+06 2.593966e+06
mean 2.017052e+15 9.658301e+00 -4.031502e+00 4.050389e+02 5.067525e-01 1.528165e+04 -1.798047e+00 1.070807e-01
std 8.959621e+08 2.753949e+01 1.108364e+02 2.423594e+00 1.417952e-01 1.304884e+02 4.354302e+00 3.313145e+00
min 2.017050e+15 -5.324562e+01 -1.799997e+02 3.838851e+02 2.190593e-01 1.505400e+04 -1.256074e+01 -1.590869e+01
25% 2.017051e+15 -1.587250e+01 -1.114549e+02 4.028636e+02 4.048825e-01 1.516700e+04 -5.229359e+00 -2.210503e+00
50% 2.017052e+15 1.095474e+01 -6.650717e+00 4.053445e+02 4.713762e-01 1.528100e+04 -2.656964e+00 1.473279e-01
75% 2.017052e+15 3.197944e+01 9.945771e+01 4.071015e+02 5.777607e-01 1.539700e+04 1.341646e+00 2.533775e+00
max 2.017053e+15 8.180215e+01 1.799994e+02 4.204220e+02 1.957203e+00 1.550400e+04 1.628201e+01 1.180915e+01
{% endraw %}

To convert the sounding_id into a datetime variable data:

{% raw %} 
from datetime import datetime
def to_date(a):
    return datetime.strptime(str(a), '%Y%m%d%H%M%S%f')

data_1610['date'] = data_1610['sounding_id'].apply(to_date)
data_1705['date'] = data_1705['sounding_id'].apply(to_date)
data_1803['date'] = data_1803['sounding_id'].apply(to_date)
data_1805['date'] = data_1805['sounding_id'].apply(to_date)
data_1808['date'] = data_1808['sounding_id'].apply(to_date)
data_1809['date'] = data_1809['sounding_id'].apply(to_date)
{% endraw %} {% raw %} 
data_1803.head()
sounding_id latitude longitude xco2 xco2_uncert orbit windspeed_u windspeed_v date
0 2018030100414377 -71.205368 -150.465195 403.405029 0.364850 19481 2.755182 -4.412096 2018-03-01 00:41:43.770
1 2018030100414407 -71.188362 -150.481216 403.444244 0.308190 19481 2.708776 -4.463214 2018-03-01 00:41:44.070
2 2018030100414437 -71.171242 -150.497299 402.874207 0.338045 19481 2.661973 -4.514851 2018-03-01 00:41:44.370
3 2018030100414477 -71.154114 -150.513336 403.447021 0.343535 19481 2.615070 -4.566681 2018-03-01 00:41:44.770
4 2018030100414537 -71.120079 -150.545151 403.163849 0.322739 19481 2.521517 -4.670267 2018-03-01 00:41:45.370
{% endraw %}

We are seaking the emission peaks taken as an example in the annexes of F. Chevallier's article Observing carbon dioxide emissions over China's cities with the Orbiting Carbon Observatory-2:

  • Over Anshan, the 17th October 2016
  • Over Baotou, the 17th May 2018
  • Over Dezhou, the 24th September 2018
  • Over Laiwu, the 25th August 2018
  • Over Nanjing, the 9th March 2018
  • Over Tangshan, the 18th May 2017

Anshan, 17th October 2016

{% raw %} 
# We consider the October 2016 datset at the right day
data_1610_1017 = data_1610[data_1610['date'] < "2016-10-18"]
data_1610_1017 = data_1610_1017[data_1610_1017['date'] > "2016-10-17"]

draw_map(data_1610_1017)
{% endraw %} {% raw %} 
# We consider the orgit going over East China
data_anshan = data_1610_1017[data_1610_1017['longitude'] > 120]
data_anshan = data_anshan[data_anshan['longitude'] < 130]

# We also spot an unwanted datapoint over Russia (point number 1613182)
data_anshan = data_anshan.drop([1613182])

data_anshan
sounding_id latitude longitude xco2 xco2_uncert orbit windspeed_u windspeed_v date
1618475 2016101704551201 39.713848 123.223122 405.396973 0.643978 12202 1.106026 -6.610551 2016-10-17 04:55:12.010
1618476 2016101704551234 39.710373 123.240562 404.653992 0.541921 12202 1.120837 -6.626420 2016-10-17 04:55:12.340
1618477 2016101704551272 39.745937 123.218620 401.544556 0.907313 12202 1.115747 -6.640970 2016-10-17 04:55:12.720
1618478 2016101704551275 39.722454 123.242020 400.760132 0.646142 12202 1.127188 -6.640725 2016-10-17 04:55:12.750
1618479 2016101704551276 39.714558 123.249611 403.785919 0.635035 12202 1.130997 -6.640931 2016-10-17 04:55:12.760
... ... ... ... ... ... ... ... ... ...
1620531 2016101704571778 47.152748 120.692909 400.722778 0.710122 12202 4.842816 -2.710787 2016-10-17 04:57:17.780
1620532 2016101704571806 47.187519 120.666435 400.493011 0.649717 12202 5.069889 -2.819871 2016-10-17 04:57:18.060
1620533 2016101704571902 47.276245 120.604439 395.362579 1.091313 12202 5.636462 -3.072844 2016-10-17 04:57:19.020
1620534 2016101704571935 47.274048 120.626297 401.645233 0.788157 12202 5.613501 -3.085500 2016-10-17 04:57:19.350
1620535 2016101704572332 47.532906 120.504341 391.299591 0.663137 12202 6.677439 -3.350218 2016-10-17 04:57:23.320

2061 rows × 9 columns

{% endraw %} {% raw %} 
# We retrieve the Figure 2.A
draw_map(data_anshan, lon_min=70, lon_max=140, lat_min=15, lat_max=55, frontier=True, size_point=5)
{% endraw %} {% raw %} 
# We represent the observation zoomed on Anshan
draw_map(data_anshan, lon_min=122, lon_max=123.7, lat_min=40, lat_max=41.7, frontier=True, size_point=5)
{% endraw %} {% raw %} 
# We restrict the data to the window studied
data_anshan_restrict = data_anshan.query('longitude>122 and longitude<123.7 and latitude>40 and latitude<41.7')

lon_min = data_anshan_restrict['longitude'].iloc[0]
lat_min = data_anshan_restrict['latitude'].iloc[0]

# We create a 'distance' column considering th distance of the measure to the minimal longitude and latitude
data_anshan_restrict['distance'] = data_anshan_restrict.apply(lambda row: haversine(row, lon_min, lat_min), axis=1)
data_anshan_restrict

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  import sys
sounding_id latitude longitude xco2 xco2_uncert orbit windspeed_u windspeed_v date distance
1618557 2016101704551701 40.012833 123.129265 401.519745 0.535533 12202 1.166155 -6.893898 2016-10-17 04:55:17.010 6757.649492
1618558 2016101704551702 40.005138 123.137276 402.293549 0.850998 12202 1.171039 -6.891319 2016-10-17 04:55:17.020 6757.895386
1618562 2016101704551731 40.032860 123.122879 400.636688 0.591878 12202 1.162234 -6.907812 2016-10-17 04:55:17.310 6756.243481
1618563 2016101704551732 40.025169 123.130890 401.229492 0.683111 12202 1.168129 -6.904029 2016-10-17 04:55:17.320 6756.489075
1618564 2016101704551733 40.017395 123.138855 402.170319 0.570602 12202 1.172991 -6.901238 2016-10-17 04:55:17.330 6756.744356
... ... ... ... ... ... ... ... ... ... ...
1619208 2016101704554576 41.687225 122.615387 401.498169 0.484547 12202 0.860433 -5.669064 2016-10-17 04:55:45.760 6639.262992
1619209 2016101704554577 41.679413 122.623375 402.182526 0.454339 12202 0.853610 -5.670618 2016-10-17 04:55:45.770 6639.522890
1619210 2016101704554578 41.671547 122.631264 401.903534 0.522504 12202 0.846649 -5.672243 2016-10-17 04:55:45.780 6639.792535
1619216 2016101704554607 41.699371 122.616951 401.880188 0.504315 12202 0.881808 -5.661693 2016-10-17 04:55:46.070 6638.130129
1619217 2016101704554608 41.691505 122.624847 401.645325 0.526542 12202 0.874808 -5.663305 2016-10-17 04:55:46.080 6638.399407

644 rows × 10 columns

{% endraw %} {% raw %} 
data_anshan_restrict.plot.scatter(x='distance', y='xco2')

<matplotlib.axes._subplots.AxesSubplot at 0x7f04538695f8>
{% endraw %}

Baotou, 17th May 2018

{% raw %} 
# We consider the May 2018 datset at the right day
data_1805_17 = data_1805[data_1805['date'] < "2018-05-18"]
data_1805_17 = data_1805_17[data_1805_17['date'] > "2018-05-17"]

draw_map(data_1805_17)
{% endraw %} {% raw %} 
# We consider the orgit going over East China (retrieved with the coordinates first, then with the orbit id)
data_baotou = data_1805_17[data_1805_17['orbit'] == 20605]

data_baotou
sounding_id latitude longitude xco2 xco2_uncert orbit windspeed_u windspeed_v date
1398272 2018051705191031 -30.556480 126.773727 403.617279 0.448212 20605 -4.640143 2.496437 2018-05-17 05:19:10.310
1398273 2018051705191071 -30.538458 126.769890 402.357697 0.475649 20605 -4.657061 2.510647 2018-05-17 05:19:10.710
1398274 2018051705191401 -30.358664 126.730782 404.189301 0.435722 20605 -4.646074 2.699590 2018-05-17 05:19:14.010
1398275 2018051705191431 -30.340662 126.726868 404.099396 0.439352 20605 -4.640612 2.719563 2018-05-17 05:19:14.310
1398276 2018051705191471 -30.322620 126.722969 402.722961 0.456355 20605 -4.635260 2.739541 2018-05-17 05:19:14.710
... ... ... ... ... ... ... ... ... ...
1408490 2018051705442806 53.289833 104.040840 409.891052 0.765022 20605 0.894804 3.061221 2018-05-17 05:44:28.060
1408491 2018051705442808 53.272186 104.065964 409.573120 0.929197 20605 0.891646 3.007289 2018-05-17 05:44:28.080
1408492 2018051705531733 77.736710 57.864475 410.171082 0.746532 20605 -5.030067 -0.571781 2018-05-17 05:53:17.330
1408493 2018051705531775 77.744179 57.948849 410.165192 0.651399 20605 -5.011450 -0.604521 2018-05-17 05:53:17.750
1408494 2018051705531806 77.752121 57.954662 411.600952 0.690810 20605 -5.005180 -0.610087 2018-05-17 05:53:18.060

10223 rows × 9 columns

{% endraw %} {% raw %} 
# We retrieve the Figure 2.A
draw_map(data_baotou, lon_min=70, lon_max=140, lat_min=15, lat_max=55, frontier=True, size_point=5)
{% endraw %} {% raw %} 
# We represent the observation zoomed on Anshan
draw_map(data_baotou, lon_min=109, lon_max=110.7, lat_min=39.7, lat_max=41.4, frontier=True, size_point=5)
{% endraw %} {% raw %} 
# We restrict the data to the window studied
data_baotou_restrict = data_baotou.query('longitude>109 and longitude<110.7and latitude>39.7 and latitude<41.4')

lon_min = data_anshan_restrict['longitude'].iloc[0]
lat_min = data_anshan_restrict['latitude'].iloc[0]

# We create a 'distance' column considering th distance of the measure to the minimal longitude and latitude
data_baotou_restrict['distance'] = data_baotou_restrict.apply(lambda row: haversine(row, lon_min, lat_min), axis=1)
data_baotou_restrict

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  import sys
sounding_id latitude longitude xco2 xco2_uncert orbit windspeed_u windspeed_v date distance
1406381 2018051705401671 39.706150 110.162216 410.447510 0.844058 20605 6.616374 -4.232733 2018-05-17 05:40:16.710 7439.732917
1406386 2018051705401701 39.724422 110.155617 411.826447 0.991852 20605 6.656465 -4.210843 2018-05-17 05:40:17.010 7438.363467
1406391 2018051705401731 39.742889 110.149147 411.029053 0.857783 20605 6.696247 -4.188884 2018-05-17 05:40:17.310 7436.969850
1406392 2018051705401732 39.733047 110.154160 411.387207 0.915564 20605 6.673672 -4.199841 2018-05-17 05:40:17.320 7437.637810
1406393 2018051705401733 39.722965 110.158920 411.238617 0.908178 20605 6.650679 -4.211333 2018-05-17 05:40:17.330 7438.339937
... ... ... ... ... ... ... ... ... ... ...
1406834 2018051705404808 41.358391 109.566971 409.935303 0.776155 20605 7.562441 -5.240523 2018-05-17 05:40:48.080 7315.715712
1406839 2018051705404836 41.396564 109.549515 410.028259 0.768482 20605 7.536522 -5.387716 2018-05-17 05:40:48.360 7313.021110
1406840 2018051705404837 41.386616 109.554855 411.170807 0.747321 20605 7.544033 -5.348670 2018-05-17 05:40:48.370 7313.686335
1406841 2018051705404838 41.376598 109.560081 410.223694 0.742845 20605 7.551347 -5.309278 2018-05-17 05:40:48.380 7314.363209
1406848 2018051705404878 41.394867 109.553192 410.353912 0.732099 20605 7.539907 -5.377571 2018-05-17 05:40:48.780 7313.005055

446 rows × 10 columns

{% endraw %} {% raw %} 
data_baotou_restrict.plot.scatter(x='distance', y='xco2')

<matplotlib.axes._subplots.AxesSubplot at 0x7f045359f828>
{% endraw %}

Dezhou, 24th September 2018

{% raw %} 
# We consider the September 2018 datset at the right day
data_1809_24 = data_1809[data_1809['date'] < "2018-09-25"]
data_1809_24 = data_1809_24[data_1809_24['date'] > "2018-09-24"]

draw_map(data_1809_24)
{% endraw %} {% raw %} 
# We consider the orgit going over East China
#data_dezhou = data_1809_24[data_1809_24['longitude'] > 115]
#data_dezhou = data_dezhou[data_dezhou['longitude'] < 125]

data_dezhou = data_1809_24[data_1809_24['orbit'] == 22498]

data_dezhou
sounding_id latitude longitude xco2 xco2_uncert orbit windspeed_u windspeed_v date
2244321 2018092405065234 -31.974575 132.433289 404.704620 0.367528 22498 -4.025777 1.476680 2018-09-24 05:06:52.340
2244322 2018092405065235 -31.976723 132.420105 404.928131 0.339298 22498 -4.032157 1.452572 2018-09-24 05:06:52.350
2244323 2018092405065237 -31.980803 132.393616 405.949554 0.387624 22498 -4.044868 1.403723 2018-09-24 05:06:52.370
2244324 2018092405065238 -31.982733 132.380356 405.249084 0.468772 22498 -4.051178 1.379073 2018-09-24 05:06:52.380
2244325 2018092405065272 -31.950018 132.454315 407.567993 0.386078 22498 -3.991433 1.458995 2018-09-24 05:06:52.720
... ... ... ... ... ... ... ... ... ...
2255035 2018092405332838 63.666580 103.865494 405.777832 1.116346 22498 1.888981 1.329452 2018-09-24 05:33:28.380
2255036 2018092405332873 63.715221 103.758492 404.681244 1.015830 22498 1.849106 1.326898 2018-09-24 05:33:28.730
2255037 2018092405332876 63.697647 103.813515 405.516846 0.910857 22498 1.863525 1.328544 2018-09-24 05:33:28.760
2255038 2018092405332877 63.691662 103.831566 405.766785 0.946319 22498 1.868399 1.329098 2018-09-24 05:33:28.770
2255039 2018092405332878 63.685604 103.849480 406.611542 1.092575 22498 1.873313 1.329655 2018-09-24 05:33:28.780

10719 rows × 9 columns

{% endraw %} {% raw %} 
# We retrieve the Figure 2.A
draw_map(data_dezhou, lon_min=70, lon_max=140, lat_min=15, lat_max=55, frontier=True, size_point=5)
{% endraw %} {% raw %} 
# We represent the observation zoomed on Anshan
draw_map(data_dezhou, lon_min=115.3, lon_max=117.1, lat_min=36.5, lat_max=38.3, frontier=True, size_point=5)
{% endraw %} {% raw %} 
# We restrict the data to the window studied
data_dezhou_restrict = data_dezhou.query('longitude>115.3 and longitude<117.1 and latitude>36.5 and latitude<38.5')

lon_min = data_dezhou_restrict['longitude'].iloc[0]
lat_min = data_dezhou_restrict['latitude'].iloc[0]

# We create a 'distance' column considering th distance of the measure to the minimal longitude and latitude
data_dezhou_restrict['distance'] = data_dezhou_restrict.apply(lambda row: haversine(row, lon_min, lat_min), axis=1)
data_dezhou_restrict

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  import sys
sounding_id latitude longitude xco2 xco2_uncert orbit windspeed_u windspeed_v date distance
2251673 2018092405254731 36.503620 116.460190 405.356506 0.653529 22498 -2.676006 -2.669888 2018-09-24 05:25:47.310 6889.582571
2251680 2018092405254771 36.523537 116.454323 405.089783 0.663060 22498 -2.673140 -2.662889 2018-09-24 05:25:47.710 6887.921659
2251681 2018092405254772 36.515282 116.460892 404.981537 0.458675 22498 -2.670804 -2.667496 2018-09-24 05:25:47.720 6888.426263
2251682 2018092405254773 36.506950 116.467392 405.349243 0.627049 22498 -2.668517 -2.672142 2018-09-24 05:25:47.730 6888.941259
2251688 2018092405254801 36.543476 116.448471 405.415070 0.543986 22498 -2.670167 -2.655999 2018-09-24 05:25:48.010 6886.257902
... ... ... ... ... ... ... ... ... ... ...
2252332 2018092405261807 38.290573 115.952286 406.297394 0.670311 22498 -2.157124 -2.219882 2018-09-24 05:26:18.070 6739.917283
2252333 2018092405261808 38.282223 115.958794 406.516113 0.684659 22498 -2.151567 -2.231655 2018-09-24 05:26:18.080 6740.434392
2252334 2018092405261837 38.310497 115.946213 404.022247 0.814230 22498 -2.163852 -2.203840 2018-09-24 05:26:18.370 6738.268611
2252335 2018092405261838 38.302151 115.952721 404.777710 0.800562 22498 -2.158177 -2.215560 2018-09-24 05:26:18.380 6738.785362
2252336 2018092405261878 38.322128 115.946686 405.811066 0.750888 22498 -2.164925 -2.199561 2018-09-24 05:26:18.780 6737.129933

651 rows × 10 columns

{% endraw %} {% raw %} 
data_dezhou_restrict.plot.scatter(x='distance', y='xco2')

<matplotlib.axes._subplots.AxesSubplot at 0x7f04537acd68>
{% endraw %}

Laiwu, 25th August 2018

{% raw %} 
# We consider the August 2018 datset at the right day
data_1808_25 = data_1808[data_1808['date'] < "2018-08-26"]
data_1808_25 = data_1808_25[data_1808_25['date'] > "2018-08-25"]

draw_map(data_1808_25)
{% endraw %} {% raw %} 
# We consider the orgit going over East China
#data_laiwu = data_1808_25[data_1808_25['longitude'] > 110]
#data_laiwu = data_laiwu[data_laiwu['longitude'] < 125]

data_laiwu = data_1808_25[data_1808_25['orbit'] == 22061]

data_laiwu
sounding_id latitude longitude xco2 xco2_uncert orbit windspeed_u windspeed_v date
2061319 2018082504501738 -43.749119 135.989822 403.642273 0.572042 22061 11.114212 -4.509858 2018-08-25 04:50:17.380
2061320 2018082504501777 -43.735889 136.010880 404.398529 0.466245 22061 11.103701 -4.486870 2018-08-25 04:50:17.770
2061321 2018082504501778 -43.731358 135.985352 404.115814 0.528921 22061 11.101484 -4.501010 2018-08-25 04:50:17.780
2061322 2018082504501806 -43.722591 136.031830 404.290619 0.449389 22061 11.093133 -4.463718 2018-08-25 04:50:18.060
2061323 2018082504501835 -43.709126 136.052704 403.948914 0.479135 22061 11.082438 -4.440311 2018-08-25 04:50:18.350
... ... ... ... ... ... ... ... ... ...
2073115 2018082505183238 49.763687 111.791290 400.929993 0.781497 22061 0.300315 2.200201 2018-08-25 05:18:32.380
2073116 2018082505214175 59.688877 105.254318 401.177765 1.203000 22061 -0.192591 0.018251 2018-08-25 05:21:41.750
2073117 2018082505214176 59.679939 105.275047 401.176849 1.284261 22061 -0.189052 0.015300 2018-08-25 05:21:41.760
2073118 2018082505214207 59.687191 105.280876 402.654144 1.082323 22061 -0.194836 0.013121 2018-08-25 05:21:42.070
2073119 2018082505214238 59.694370 105.286636 403.211151 1.157874 22061 -0.200499 0.010925 2018-08-25 05:21:42.380

11801 rows × 9 columns

{% endraw %} {% raw %} 
# We retrieve the Figure 2.A
draw_map(data_laiwu, lon_min=70, lon_max=140, lat_min=15, lat_max=55, frontier=True, size_point=5)
{% endraw %} {% raw %} 
# We represent the observation zoomed on Anshan
draw_map(data_laiwu, lon_min=116.5, lon_max=118.2, lat_min=35.4, lat_max=37.1, frontier=True, size_point=5)
{% endraw %} {% raw %} 
# We restrict the data to the window studied
data_laiwu_restrict = data_laiwu.query('longitude>116.5 and longitude<118.2  and latitude>35.4 and latitude<37.1')

lon_min = data_laiwu_restrict['longitude'].iloc[0]
lat_min = data_laiwu_restrict['latitude'].iloc[0]

# We create a 'distance' column considering th distance of the measure to the minimal longitude and latitude
data_laiwu_restrict['distance'] = data_laiwu_restrict.apply(lambda row: haversine(row, lon_min, lat_min), axis=1)
data_laiwu_restrict

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  import sys
sounding_id latitude longitude xco2 xco2_uncert orbit windspeed_u windspeed_v date distance
2069837 2018082505140774 35.429119 117.594986 400.076141 0.716316 22061 -2.914134 -0.235950 2018-08-25 05:14:07.740 6945.696315
2069840 2018082505140802 35.467060 117.577034 398.184967 0.684366 22061 -2.897508 -0.184848 2018-08-25 05:14:08.020 6942.928136
2069841 2018082505140803 35.457211 117.582947 400.502319 0.685602 22061 -2.902577 -0.197750 2018-08-25 05:14:08.030 6943.588047
2069842 2018082505140835 35.455803 117.588387 401.529999 0.628872 22061 -2.905890 -0.198751 2018-08-25 05:14:08.350 6943.466947
2069843 2018082505140837 35.436092 117.599686 400.578766 0.576075 22061 -2.915572 -0.225130 2018-08-25 05:14:08.370 6944.812476
... ... ... ... ... ... ... ... ... ... ...
2070385 2018082505143808 37.056301 117.058731 401.634552 0.652954 22061 -2.511884 1.644871 2018-08-25 05:14:38.080 6816.151165
2070391 2018082505143836 37.094391 117.039970 402.149200 0.752567 22061 -2.441297 1.709189 2018-08-25 05:14:38.360 6813.409651
2070392 2018082505143837 37.084496 117.046188 402.232849 0.695273 22061 -2.459902 1.692454 2018-08-25 05:14:38.370 6814.059565
2070393 2018082505143838 37.074539 117.052299 401.568298 0.741098 22061 -2.478597 1.675727 2018-08-25 05:14:38.380 6814.720206
2070400 2018082505143878 37.092781 117.045906 401.461151 0.689398 22061 -2.445320 1.706824 2018-08-25 05:14:38.780 6813.287147

548 rows × 10 columns

{% endraw %} {% raw %} 
data_laiwu_restrict.plot.scatter(x='distance', y='xco2')

<matplotlib.axes._subplots.AxesSubplot at 0x7f045361dfd0>
{% endraw %}

Nanjing, 9th March 2018

{% raw %} 
# We consider the March 2018 datset at the right day
data_1803_09 = data_1803[data_1803['date'] < "2018-03-10"]
data_1803_09 = data_1803_09[data_1803_09['date'] > "2018-03-09"]

draw_map(data_1803_09)
{% endraw %} {% raw %} 
# We consider the orgit going over East China
#data_nanjing = data_1803_09[data_1803_09['longitude'] > 115]
#data_nanjing = data_nanjing[data_nanjing['longitude'] < 125]

data_nanjing = data_1803_09[data_1803_09['orbit'] == 19600]

data_nanjing
sounding_id latitude longitude xco2 xco2_uncert orbit windspeed_u windspeed_v date
774288 2018030905013078 -32.893936 134.095108 405.924896 0.410294 19600 -2.972941 1.491366 2018-03-09 05:01:30.780
774289 2018030905013101 -32.861305 134.184021 407.432404 0.430512 19600 -3.270708 1.225287 2018-03-09 05:01:31.010
774290 2018030905013102 -32.863373 134.170654 406.648499 0.423339 19600 -3.232368 1.264700 2018-03-09 05:01:31.020
774291 2018030905013103 -32.865364 134.157196 406.607483 0.472549 19600 -3.194245 1.303926 2018-03-09 05:01:31.030
774292 2018030905013104 -32.867268 134.143661 406.519135 0.412724 19600 -3.156398 1.342928 2018-03-09 05:01:31.040
... ... ... ... ... ... ... ... ... ...
777155 2018030905242874 50.047482 113.196968 409.765564 0.565351 19600 1.940813 -3.650892 2018-03-09 05:24:28.740
777156 2018030905242875 50.041122 113.208916 410.043793 0.876736 19600 1.893785 -3.626530 2018-03-09 05:24:28.750
777157 2018030905242876 50.034679 113.220795 408.826447 0.599787 19600 1.847021 -3.602393 2018-03-09 05:24:28.760
777158 2018030905242904 50.067184 113.188614 408.737579 0.777149 19600 1.995163 -3.641963 2018-03-09 05:24:29.040
777159 2018030905242905 50.060825 113.200569 408.314484 0.867150 19600 1.947807 -3.617803 2018-03-09 05:24:29.050

2872 rows × 9 columns

{% endraw %} {% raw %} 
# We retrieve the Figure 2.A
draw_map(data_nanjing, lon_min=70, lon_max=140, lat_min=15, lat_max=55, frontier=True, size_point=5)
{% endraw %} {% raw %} 
# We represent the observation zoomed on Anshan
draw_map(data_nanjing, lon_min=118.3, lon_max=120, lat_min=31.3, lat_max=33, frontier=True, size_point=5)
{% endraw %} {% raw %} 
# We restrict the data to the window studied
data_nanjing_restrict = data_nanjing.query('longitude>118.3 and longitude<120  and latitude>31.3 and latitude<33')

lon_min = data_nanjing_restrict['longitude'].iloc[0]
lat_min = data_nanjing_restrict['latitude'].iloc[0]

# We create a 'distance' column considering th distance of the measure to the minimal longitude and latitude
data_nanjing_restrict['distance'] = data_nanjing_restrict.apply(lambda row: haversine(row, lon_min, lat_min), axis=1)
data_nanjing_restrict

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  import sys
sounding_id latitude longitude xco2 xco2_uncert orbit windspeed_u windspeed_v date distance
775486 2018030905191472 31.313959 119.354256 412.440643 0.941768 19600 1.354087 -1.728690 2018-03-09 05:19:14.720 7114.317244
775490 2018030905191501 31.341694 119.341812 411.871063 0.896037 19600 1.347316 -1.699039 2018-03-09 05:19:15.010 7112.376377
775491 2018030905191503 31.326025 119.355827 410.738647 0.951694 19600 1.355291 -1.723064 2018-03-09 05:19:15.030 7113.111125
775492 2018030905191533 31.346003 119.350426 410.515259 0.956639 19600 1.352511 -1.705437 2018-03-09 05:19:15.330 7111.528725
775493 2018030905191534 31.338078 119.357338 410.892853 1.011362 19600 1.356424 -1.717422 2018-03-09 05:19:15.340 7111.909458
... ... ... ... ... ... ... ... ... ... ...
775815 2018030905194278 32.948311 118.942467 410.990753 0.920671 19600 1.255895 -0.361807 2018-03-09 05:19:42.780 6983.534551
775818 2018030905194304 32.999649 118.908806 410.445251 0.869109 19600 1.229260 -0.317407 2018-03-09 05:19:43.040 6980.498971
775819 2018030905194307 32.976219 118.930099 410.147675 0.974762 19600 1.246704 -0.339534 2018-03-09 05:19:43.070 6981.579663
775820 2018030905194308 32.968296 118.937035 410.604004 0.893977 19600 1.252049 -0.347317 2018-03-09 05:19:43.080 6981.958728
775823 2018030905194337 32.996193 118.924644 411.277649 0.886598 19600 1.242222 -0.325789 2018-03-09 05:19:43.370 6980.006151

331 rows × 10 columns

{% endraw %} {% raw %} 
data_nanjing_restrict.plot.scatter(x='distance', y='xco2')

<matplotlib.axes._subplots.AxesSubplot at 0x7f045352ac18>
{% endraw %}

Tangshan, 18th March 2017

{% raw %} 
# We consider the August 2018 datset at the right day
data_1705_18 = data_1705[data_1705['date'] < "2017-05-19"]
data_1705_18 = data_1705_18[data_1705_18['date'] > "2017-05-18"]

draw_map(data_1705_18)
{% endraw %} {% raw %} 
# We consider the orgit going over East China
#data_tangshan = data_1705_18[data_1705_18['longitude'] > 110]
#data_tangshan = data_tangshan[data_tangshan['longitude'] < 125]

data_tangshan = data_1705_18[data_1705_18['orbit'] == 15304]

data_tangshan
sounding_id latitude longitude xco2 xco2_uncert orbit windspeed_u windspeed_v date
1435063 2017051804535708 -34.499416 136.092346 402.725983 0.441209 15304 -3.124291 0.773405 2017-05-18 04:53:57.080
1435064 2017051804535737 -34.480766 136.101074 401.561615 0.435428 15304 -3.095217 0.765590 2017-05-18 04:53:57.370
1435065 2017051804535738 -34.479500 136.086716 402.021851 0.429804 15304 -3.096632 0.767331 2017-05-18 04:53:57.380
1435066 2017051804535777 -34.460827 136.095459 401.459717 0.451545 15304 -3.067521 0.759512 2017-05-18 04:53:57.770
1435067 2017051804535778 -34.459560 136.081116 400.935150 0.475128 15304 -3.068939 0.761223 2017-05-18 04:53:57.780
... ... ... ... ... ... ... ... ... ...
1442739 2017051805213831 65.030724 105.592751 405.706451 0.921386 15304 0.966269 -1.750624 2017-05-18 05:21:38.310
1442740 2017051805213901 65.068771 105.557571 407.058624 0.874896 15304 0.945448 -1.749902 2017-05-18 05:21:39.010
1442741 2017051805213932 65.082306 105.559853 402.380981 0.642988 15304 0.941750 -1.752096 2017-05-18 05:21:39.320
1442742 2017051805214005 65.103180 105.583496 406.184021 0.512709 15304 0.941109 -1.758598 2017-05-18 05:21:40.050
1442743 2017051805214034 65.127747 105.546227 406.881104 0.617333 15304 0.923979 -1.756151 2017-05-18 05:21:40.340

7681 rows × 9 columns

{% endraw %} {% raw %} 
# We retrieve the Figure 2.A
draw_map(data_tangshan, lon_min=70, lon_max=140, lat_min=15, lat_max=55, frontier=True, size_point=5)
{% endraw %} {% raw %} 
# We represent the observation zoomed on Anshan
draw_map(data_tangshan, lon_min=117.8, lon_max=119.1, lat_min=39.2, lat_max=40.6, frontier=True, size_point=5)
{% endraw %} {% raw %} 
# We restrict the data to the window studied
data_tangshan_restrict = data_tangshan.query('longitude>117.8 and longitude<119.1  and latitude>39.2 and latitude<40.6')

lon_min = data_tangshan_restrict['longitude'].iloc[0]
lat_min = data_tangshan_restrict['latitude'].iloc[0]

# We create a 'distance' column considering th distance of the measure to the minimal longitude and latitude
data_tangshan_restrict['distance'] = data_tangshan_restrict.apply(lambda row: haversine(row, lon_min, lat_min), axis=1)
data_tangshan_restrict

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  import sys
sounding_id latitude longitude xco2 xco2_uncert orbit windspeed_u windspeed_v date distance
1441293 2017051805142031 39.268059 118.659294 409.865662 0.792093 15304 1.955974 4.938334 2017-05-18 05:14:20.310 6766.578503
1441294 2017051805142032 39.258781 118.663086 410.766327 0.496066 15304 1.902134 4.912851 2017-05-18 05:14:20.320 6767.270741
1441295 2017051805142033 39.249439 118.666801 411.688782 0.534073 15304 1.847724 4.887047 2017-05-18 05:14:20.330 6767.972598
1441296 2017051805142071 39.287979 118.653069 408.824860 0.832045 15304 2.072734 4.994613 2017-05-18 05:14:20.710 6765.003114
1441297 2017051805142072 39.278709 118.656891 410.028320 0.666536 15304 2.019178 4.969284 2017-05-18 05:14:20.720 6765.693207
... ... ... ... ... ... ... ... ... ... ...
1441554 2017051805144234 40.555550 118.256111 409.701538 0.433667 15304 4.114278 4.674913 2017-05-18 05:14:42.340 6664.883418
1441555 2017051805144272 40.593811 118.241089 412.106232 0.554296 15304 4.037082 4.533223 2017-05-18 05:14:42.720 6662.003725
1441556 2017051805144275 40.566322 118.253983 410.119080 0.386038 15304 4.092721 4.634257 2017-05-18 05:14:42.750 6663.976080
1441557 2017051805144276 40.557110 118.258072 410.099548 0.440312 15304 4.110785 4.668066 2017-05-18 05:14:42.760 6664.647737
1441559 2017051805144304 40.595425 118.243523 411.841583 0.593447 15304 4.033669 4.526024 2017-05-18 05:14:43.040 6661.741378

266 rows × 10 columns

{% endraw %} {% raw %} 
data_tangshan_restrict.plot.scatter(x='distance', y='xco2')

<matplotlib.axes._subplots.AxesSubplot at 0x7f04539a1518>
{% endraw %}